Evaluating recommender systems

By Wen Wu, Liang He, Jing Yang. International Conference on Digital Information Management, 2012

427 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/49628b754b7a54c8b9d0b15f27a20b20bb7f23ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Emilia G'omez
2. Carlos Castillo
3. Maurizio Morisio
4. Erion Çano

In Information Systems

1. Emilia G'omez
2. Carlos Castillo
3. Maurizio Morisio
4. Erion Çano
5. Dougal Shakespeare
6. Lorenzo Porcaro

In Management Science and Operations Research

1. Emilia G'omez
2. Carlos Castillo
3. Dougal Shakespeare
4. Lorenzo Porcaro

Citations in the corpus

• Hybrid recommender systems: A systematic literature review (2017, background): “The evaluation process steps are clearly explained in [39].”
• Exploring Artist Gender Bias in Music Recommendation (2020, background): “Evaluating recommender systems is a non-trivial task because of the multiple facets that a good recommendation can have, and the multiple players influencing these aspects [20].”